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Ahmad Firdaus

Researcher at Universiti Malaysia Pahang

Publications -  37
Citations -  688

Ahmad Firdaus is an academic researcher from Universiti Malaysia Pahang. The author has contributed to research in topics: Malware & Computer science. The author has an hindex of 10, co-authored 27 publications receiving 354 citations. Previous affiliations of Ahmad Firdaus include University of Malaya.

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The rise of “blockchain”: bibliometric analysis of blockchain study

TL;DR: Keyword analysis revealed that researchers are adopting blockchain to solve problems in multiple categories of the data research area (data privacy, digital storage, the security of data, big data, and distributed database) and highlighted the utilization and consensus of the algorithm in blockchain research.
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The rise of malware

TL;DR: The number of papers published by Asian countries such as China, Korea, India, Singapore and Malaysia in relation to the Middle East and North America is discussed and there are several significant impacts of research activities in Asia, in comparison to other continents.
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Blending Big Data Analytics: Review on Challenges and a Recent Study

TL;DR: This paper explores how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce, and investigates case studies of big data Analytics on various techniques of such analytics, namely, text, voice, video, and network analytics.
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Discovering optimal features using static analysis and a genetic search based method for Android malware detection

TL;DR: Using genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings, machine learning classifiers were used to evaluate the best features determined by GS, namely, Naïve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer perceptron (MLP).
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Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management

TL;DR: This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB) to enhance the machine learning prediction that detects unknown root exploit.